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A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis

机译:通过球形自组织图鉴定细胞表面标志物的独特方法应用于DNA微阵列分析

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摘要

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.
机译:为了鉴定细胞特异性标记,我们设计了带有用于人膜锚定蛋白的寡核苷酸探针的DNA微阵列平台。使用微阵列分析人神经胶质瘤细胞系,并将其与正常和胎儿脑组织进行比较。对于微阵列分析,我们采用了球形自组织图,这是一种适合于将多维数据转换为二维数据并在球形表面上显示关系的聚类方法。根据基因表达谱,将细胞表面特征成功地镜像到球形表面上,从而根据基因表达的强度将正常脑组织与疾病模型区分开。通过聚合酶链反应程序和胶质瘤细胞的免疫细胞化学染色进一步分析了聚集的胶质瘤特异性基因。我们的平台和以下程序已成功证明可对神经胶质瘤细胞特有的细胞表面蛋白编码基因进行分类。我们的评估表明,球形自组织图是区分细胞表面标记物的有价值的工具,可用于标记物发现研究中以治疗癌症。

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